Research Scientist - AI Agent Memory Infrastructure - Global Tech Research Program - 2027 Start (PhD)

Team Introduction: Join ByteDance's AI Agent Memory Infrastructure team, where we build the core memory systems that power next-generation intelligent agents. Our focus is on creating a unified platform for long-term, conversational, and task-oriented memory, enabling more personalized and context-aware AI experiences.

We design and operate large-scale, low-latency, and highly reliable memory infrastructure, covering the full lifecycle from storage and retrieval to updating and optimization. Working at the intersection of LLMs, data systems, and context engineering, we tackle challenges in memory representation, retrieval, and multimodal fusion.

Partnering closely with model and product teams, we turn advanced research into scalable production systems that support a wide range of AI-driven applications.

We are looking for talented individuals to join our team in 2027. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company.

Successful candidates must be able to commit to an onboarding date by end of year 2027. Please state your availability and graduation date clearly in your resume.

Responsibilities

• Design, build, and evolve the next-generation memory infrastructure for AI agents, developing a unified platform that supports long-term memory, conversational memory, and task-oriented memory.
• Architect and optimize memory system pipelines for large-scale, low-latency, and high-availability environments, including data ingestion, storage, indexing, retrieval, updating, compression, and forgetting mechanisms to support real-time inference and personalized interactions.
• Explore key challenges at the intersection of large language models, context engineering, and data management, including memory representation, retrieval and ranking, conflict resolution, summarization and fusion, and memory lifecycle management.
• Design unified memory models and processing workflows for multimodal data (text, image, audio, behavioral signals), enhancing agents' long-term consistency, personalization, and task completion in complex scenarios.
• Collaborate closely with model, application, and platform teams to productionize memory capabilities, and continuously optimize system performance across quality, latency, cost, reliability, and safety.
• Stay up-to-date with cutting-edge advancements and contribute to the long-term technical roadmap of AI agent memory systems, driving innovation and capability evolution.

Topic Content: With the large-scale adoption of LLMs and AI agents, traditional cloud-native infrastructure can no longer meet the ultra-high performance and elasticity requirements of AI workloads. This topic conducts systematic research across the entire AI infrastructure stack:

1. Network and Observability: Research intelligent fault localization and root cause analysis for large-scale AI clusters, combined with intelligent tuning of time-series databases to improve cluster stability.
2. Storage Systems: Develop serverless high-performance elastic file systems and storage acceleration architectures specifically for AI scenarios, explore hardware-software co-optimization for DPU, and overcome AI storage performance bottlenecks.
3. Data Center Power Scheduling: Research GPU/CPU/MEM heterogeneous collaborative scheduling technologies, build a heterogeneous power orchestration system for AI agents, and address scheduling challenges including heterogenous workloads and state dependencies.
4. Vector Retrieval: Optimize core vector retrieval technologies for LLM-powered applications, building a cloud-native distributed vector index engine to meet ultra-large-scale vector retrieval demands with low latency and low cost.
5. Intelligence and Agent Architecture: Explore automatic infrastructure optimization based on AI Agent workflows, build a self-evolvable business agent framework, and enable full-stack intelligent optimization through AI for Infra.

This topic aims to build a next-generation AI-native infrastructure to support the deployment of LLMs and AI agents, improve resource utilization, reduce costs, support elastic scaling, and drive the technological evolution of AI infrastructure.Minimum Qualifications:

• Individuals who are completing or recently completed a PhD in Software Development, Computer Science, Computer Engineering, Artificial Intelligence or a related technical discipline.
• Strong experience in distributed systems, databases, information retrieval systems, or AI infrastructure, with proven system design and production engineering capabilities.
• Proficient in at least one programming language such as Go, Python, or C++, with strong coding standards and engineering best practices.
• Solid understanding of core technologies in LLM applications, including but not limited to embeddings, retrieval-augmented generation (RAG), context engineering, retrieval systems, and long-term state management.
• Familiarity with one or more key areas in memory systems: memory extraction and representation, vector/graph indexing, retrieval and ranking, memory updating, compression and forgetting, multimodal memory fusion.

Preferred Qualifications:

• Experience in agent memory systems, user profiling, recommendation/search feature platforms, or knowledge base systems.
• Contributions to or deep understanding of open-source memory frameworks such as mem0, memOS, memU, or similar solutions.
• Strong track record in databases, information retrieval, machine learning, or AI systems, including publications, impactful open-source work, or notable technical achievements.
• Experience in multimodal data processing, online inference systems, personalized agents, or long-term user state modeling.
• Ability to analyze and optimize trade-offs across system performance, latency, cost, and scalability from both system and algorithm perspectives; experience with complex production systems is highly preferred.